Smart de-identification using date jittering

ABSTRACT

System and method to produce an anonymized cohort having less than a predetermined risk of re-identification. The method includes receiving a data query of requested traits for the anonymized cohort, querying a data source to find records that possess at least some of the traits, forming a dataset from at least some of the records, and grouping the dataset in time into a first boundary group, a second boundary group, and one or more non-boundary groups temporally between the first boundary group and second boundary group. For each non-boundary group, calculating maximum time limits the non-boundary group can be time-shifted without overlapping an adjacent group, calculating a group jitter amount, capping the group jitter amount by the maximum time limits and by respective predetermined jitter limits, and jittering said non-boundary group by the capped group jitter amount to produce an anonymized dataset. Return the anonymized dataset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/802,405, filed on Feb. 26, 2020, which is a continuation of U.S.patent application Ser. No. 16/398,771, filed on Apr. 30, 2019, now U.S.Pat. No. 10,586,074, which is a continuation of U.S. patent applicationSer. No. 15/385,710, filed on Dec. 20, 2016, now U.S. Pat. No.10,318,763 the entire contents of which are hereby incorporated byreference in their entirety.

BACKGROUND Field

The present disclosure relates to risk assessment of datasets and inparticular to reducing re-identification risk of a dataset.

Description of Related Art

Personal information is continuously captured in a multitude ofelectronic databases. Details about health, financial status and buyinghabits are stored in databases managed by public and private sectororganizations. These databases contain information about millions ofpeople, which can provide valuable research, epidemiologic and businessinsight. For example, examining a drugstore chain's prescriptions canindicate where a flu outbreak is occurring. To extract or maximize thevalue contained in these databases, data custodians often must provideoutside organizations access to their data. In order to protect theprivacy of the people whose data is being analyzed, a data custodianwill “de-identify” or “anonymize” information before releasing it to athird-party. An important type of de-identification ensures that datacannot be traced to the person about whom it pertains, this protectsagainst “identity disclosure”.

When de-identifying records, removing just direct identifiers such asnames and addresses is not sufficient to protect the privacy of thepersons whose data is being released. The problem of de-identificationinvolves personal details that are not obviously identifying. Thesepersonal details, known as quasi-identifiers, include the person's age,sex, postal code, profession, ethnic origin and income, financialtransactions, medical procedures, adjusting dates of events, and soforth. De-identification of data requires an assessment of the risk ofre-identification.

Dates, whether they represent dates of birth, visit dates, treatmentdates or a date associated with another type of event, also can be usedto identify an individual. In addition to the dates themselves, theinterval of time between pairs of dates also can be used to identify anindividual. Thus, including either the exact dates or even dates with asimple shift having been applied (i.e., two or more dates that are movedtogether by an identical amount and direction), increases a risk thatindividuals in the dataset may be re-identified.

Once the risk is determined, the risk may be reduced if necessary byperturbing the data. Perturbation is a risk mitigation technique thatchanges a field value in a dataset in order to lower risk. For example,suppose a re-identification risk of a database is measured. If themeasured risk needs to be lowered, perturbing the data may modify afield in the database by replacing actual data in the field with a valuedifferent but similar to an original value of the data in the field.However, if perturbation is not done intelligently, the perturbation mayintroduce problems in a returned dataset.

Previous methods to anonymize dates suffered one or more of thefollowing problems: First, such methods were prone to interval attacks,i.e., if the interval of time between a pair of dates remained unchangedsuch as by application of a simple shift, this provided to an attackeran opportunity to re-identify individuals.

Second, such methods suffered from a loss of analytic value, e.g.,interval information would be completely lost due toover-generalization, by grouping dates into large buckets. For example,clients may want to analyze exact dates. Methods of the background artmay overgeneralize patient data just to the year, e.g., events occurringon Jan. 1, 2015 and Dec. 31, 2015 would be over-generalized to be listedonly as occurring in 2015. Over-generalized data makes it impossible toperform some important analysis like, was the patient readmitted withinthree days, or 30 days of going to a hospital.

Third, such methods did not allow for incremental order integrity. Forexample, sets of successive dates might have been shifted, and althoughthey might have maintained their order internally, they might lose theirexternal order. For example, if one pair of dates (A1, A2) and a secondpair of events (B1, B2) had an external date order before anonymizationof A1-A2-B1-B2, prior methods of the known art might anonymize the datesto have an external date order after anonymization of A1-B1-A2-B2.

Fourth, such methods suffered from date drift, e.g., very large sets ofdates would have dates shifted far into the past or future in anunconstrained manner. For example, some data sets suffering from datadrift would accordion in or out (e.g., get smaller or get bigger),depending upon how random numbers controlling the process weregenerated.

Accordingly, systems and methods that enable lowered risk ofre-identification remains highly desirable.

BRIEF SUMMARY

Embodiments, when paired with a date shifting process, solves theproblems of the background art noted above, to achieve a high level ofanalytic value as well as a justifiably low risk of re-identification.

Embodiments in accordance with the present disclosure provide otherimportant features. For example, embodiments are repeatable, i.e., ifgiven the same input and secret jitter key, embodiments will produce thesame output. In contrast, methods of the background art would rely onsimple random noise to adjust dates. Simple random noise by itselfdefeats a second important feature of incremental ordering integrity.Ordering integrity means that, if two or more successive batches ordates have the process applied to them, the batches or dates willmaintain their ordering relative to each other. Ordering integrity alsocan be bound parametrically to ensure that adjusted dates do not sufferfrom a “date drift” problem.

Embodiments in accordance with the present disclosure may alter lengthsof intervals between pairs of patient related dates, such that anattacker could not use known intervals as an attack vector tore-identify individuals in a dataset.

Embodiments in accordance with the present disclosure include a systemand a method to produce an anonymized cohort, members of the cohorthaving less than a predetermined risk of re-identification. The systemincludes a communication interface to a database of medical data and aprocessor coupled to a memory and to the database, the memory storinginstructions to be executed by the processor, the instructions causingthe processor to perform the steps of receiving a data query via auser-facing communication channel to request an anonymized cohort, thedata query comprising requested traits to include in members of thecohort, querying a data source, using a data query transmitted via adata source-facing communication channel, to find data records thatpossess at least some of the traits, forming a dataset from at leastsome of the data records, and grouping the data records in time into afirst boundary group, a second boundary group, and one or morenon-boundary groups temporally between the first boundary group andsecond boundary group. For each non-boundary group within the dataset,the processor performs the steps of calculating maximum positive andnegative time limits said non-boundary group can be time-shifted withoutoverlapping an adjacent group, calculating a group jitter amount,capping the group jitter amount by the maximum positive and negativetime limits, and jittering the non-boundary group by the capped groupjitter. The anonymized dataset then is provided to a user via auser-facing communication channel.

The preceding is a simplified summary of embodiments of the disclosureto provide an understanding of some aspects of the disclosure. Thissummary is neither an extensive nor exhaustive overview of thedisclosure and its various embodiments. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other embodimentsof the disclosure are possible utilizing, alone or in combination, oneor more of the features set forth above or described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and still further features and advantages of the presentdisclosure will become apparent upon consideration of the followingdetailed description of embodiments thereof, especially when taken inconjunction with the accompanying drawings wherein like referencenumerals in the various figures are utilized to designate likecomponents, and wherein:

FIG. 1 shows a representation of a sample population in accordance withan embodiment of the present disclosure;

FIG. 2 shows a representation of system for determiningre-identification risk of dataset in accordance with an embodiment ofthe present disclosure;

FIG. 3A shows a data set at intermediate processing points in accordancewith an embodiment of the present disclosure;

FIG. 3B shows a process flow diagram in accordance with an embodiment ofthe present disclosure;

FIG. 4A shows a data set at intermediate processing points in accordancewith an embodiment of the present disclosure; and

FIG. 4B shows a process flow diagram in accordance with an embodiment ofthe present disclosure.

The headings used herein are for organizational purposes only and arenot meant to be used to limit the scope of the description or theclaims. As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). Similarly, the words“include”, “including”, and “includes” mean including but not limitedto. To facilitate understanding, like reference numerals have been used,where possible, to designate like elements common to the figures.Optional portions of the figures may be illustrated using dashed ordotted lines, unless the context of usage indicates otherwise.

DETAILED DESCRIPTION

The disclosure will be illustrated below in conjunction with anexemplary computing and storage system. Although well suited for usewith, e.g., a system using a server(s), data sources and/or database(s),the disclosure is not limited to use with any particular type ofcomputing, communication and storage system or configuration of systemelements. Those skilled in the art will recognize that the disclosedtechniques may be used in any computing, communication and storageapplication in which it is desirable to store protected data, such asmedical data, financial data, educational records data, etc.

As used herein, the term “module” refers generally to a logical sequenceor association of steps, processes or components. For example, asoftware module may include a set of associated routines or subroutineswithin a computer program. Alternatively, a module may comprise asubstantially self-contained hardware device. A module may also includea logical set of processes irrespective of any software or hardwareimplementation.

A module that performs a function also may be referred to as beingconfigured to perform the function, e.g., a data module that receivesdata also may be described as being configured to receive data.Configuration to perform a function may include, for example: providingand executing sets of computer code in a processor that performs thefunction; providing provisionable configuration parameters that control,limit, enable or disable capabilities of the module (e.g., setting aflag, setting permissions, setting threshold levels used at decisionpoints, etc.); providing or removing a physical connection, such as ajumper to select an option, or to enable/disable an option; attaching aphysical communication link; enabling a wireless communication link;providing electrical circuitry that is designed to perform the functionwithout use of a processor, such as by use of discrete components and/ornon-CPU integrated circuits; setting a value of an adjustable component(e.g., a tunable resistance or capacitance, etc.), energizing a circuitthat performs the function (e.g., providing power to a transceivercircuit in order to receive data); providing the module in a physicalsize that inherently performs the function (e.g., an RF antenna whosegain and operating frequency range is determined or constrained by thephysical size of the RF antenna, etc.), and so forth.

As used herein, the term “transmitter” may generally include any device,circuit, or apparatus capable of transmitting a signal. As used herein,the term “receiver” may generally include any device, circuit, orapparatus capable of receiving a signal. As used herein, the term“transceiver” may generally include any device, circuit, or apparatuscapable of transmitting and receiving a signal. As used herein, the term“signal” may include one or more of an electrical signal, a radiosignal, an optical signal, an acoustic signal, and so forth.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium excludes a computer readable signal medium such as apropagating signal. A computer readable storage medium may be, forexample, but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: a portable computer diskette, a hard disk,a random access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain, or store a program for use byor in connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Embodiments are described below, by way of example only, with referenceto FIGS. 1-4 . The exemplary systems and methods of this disclosure willalso be described in relation to software, modules, and associatedhardware. However, to avoid unnecessarily obscuring the presentdisclosure, the following description omits well-known structures,components and devices that may be shown in block diagram form, are wellknown, or are otherwise summarized.

Certain sensitive personal information like patient health informationis protected by law (e.g., Healthcare Information Portability andAccountability Act (“HIPAA,” codified at 42 U.S.C. § 300gg and 29 U.S.C§ 1181 et seq. and 42 USC 1320d et seq.) in the U.S.) and must betreated in a way that maintains patient privacy. Such information istermed protected health information (PHI). With respect to PHI, it isimportant to avoid disclosing the PHI of a specific patient, or todisclose PHI so specific that it discloses an identity of a specificpatient. All stakeholders involved must accept their stewardship rolefor protecting the PHI data contained within. It is essential thatsystems that access the PHI do so in full compliance with HIPAA and anyother applicable laws or regulations of the country concerned, and in asecure manner.

Patient information, including PHI, is sometimes needed for medicalstudies. For example, observational studies are an important category ofstudy designs. For some kinds of investigative questions (e.g., relatedto plastic surgery), randomized controlled trials may not always beindicated or ethical to conduct. Instead, observational studies may bethe next best method to address these types of questions. Well-designedobservational studies may provide results similar to randomizedcontrolled trials, challenging the belief that observational studies aresecond-rate. Cohort studies and case-control studies are two primarytypes of observational studies that aid in evaluating associationsbetween diseases and exposures.

Three types of observational studies include cohort studies,case-control studies, and cross-sectional studies. Case-control andcohort studies offer specific advantages by measuring disease occurrenceand its association with an exposure by offering a temporal dimension(i.e. prospective or retrospective study design). Cross-sectionalstudies, also known as prevalence studies, examine the data on diseaseand exposure at one particular time point. Because the temporalrelationship between disease occurrence and exposure cannot beestablished, cross-sectional studies cannot assess the cause and effectrelationship.

Cohort studies may be prospective or retrospective. Retrospective cohortstudies are well-suited for timely and inexpensive study design.Retrospective cohort studies, also known as historical cohort studies,are carried out at the present time and look to the past to examinemedical events or outcomes. A cohort of subjects, selected based onexposure status, is chosen at the present time, and outcome data (i.e.disease status, event status), which was measured in the past, arereconstructed for analysis. An advantage of the retrospective studydesign analysis is the immediate access to the data. The study design iscomparatively less costly and shorter than prospective cohort studies.However, disadvantages of retrospective study design include limitedcontrol the investigator has over data collection. The existing data maybe incomplete, inaccurate, or inconsistently measured between subjects,for example, by not being uniformly recorded for all subjects.

Some medical studies, such as retrospective cohort studies, may involveauthorized access by medical researchers to anonymized PHI, i.e., to PHIthat ideally is not identifiable with the original patient. However, inpractice there is nonzero risk that the anonymized data may bere-identified back to the original patient, for example, if dataselection criteria is excessively narrow, thus risking that a very smallpool of patients meet the selection criteria.

Databases or datasets generated therefrom that contain personallyidentifiable information such as those used in medical and financialinformation can include a cross-sectional data (L1) in addition tolongitudinal data (L2). Cross-sectional data includes a single recordfor each subject. A dataset is longitudinal if it contains multiplerecords related to each subject and the number of records may varysubject to subject. For example, part of a longitudinal dataset couldcontain specific patients and their medical results over a period ofyears. Each patient may have varying times and number of visits. Ingeneral a patient will only have a single gender, birthday, orethnicity, which is consistent throughout his/her life. Longitudinaldata are those values which exist an unknown number of times perpatient. A patient may receive only a single diagnosis, or may bediagnosed with multiple different diseases. Some patients may not haveany values for some longitudinal quasi-identifiers (QIs). An L2 grouprefers generically to a set of values drawn from one or morelongitudinal tables which can be relationally linked together. A datasetmay have more than one L2 group which cannot be inter-connected.

Such datasets are valuable in research and analytics, however the use ofthe datasets can provide an opportunity for attackers to determinepersonally identifiable information resulting in a data breach. Inmedical databases a patient can have multiple events based upon forexample diagnoses, procedures, or medical visits defining L2 data.

Traditionally, if a risk of re-identification for a dataset is estimatedto be too high (compared to a settable threshold), the estimated risk isreduced by use of one or more of several techniques to perturb the data,such as suppressing the search results entirely, intentional suppressionof specific matching returned records, inclusion of patient data from awider selection criteria (e.g., a wider age band), intentionallyreturning patient records that do not meet all of the selectioncriteria, shifting dates of events and so forth. However, thesetechniques necessarily degrade the returned data, with resulting effectson any findings based upon the degraded returned data.

Shifting considers events that are closely related temporally as forminga single group, and applies a shift of X days either backward or forwardto the entire group. Date shifting processes of the background art didnot enforce bounded time intervals for the shifted dates. Methods of thebackground introduced greater variability between dates as the overallnumber of dates processed increased. For example, suppose a patientrecord includes a first event on Jan. 1, 2015, then 20 intermediatehospital visits, and a final event on Mar. 30, 2016. Under methods ofthe background art, the end dates (e.g., Jan. 1, 2015 and Mar. 30, 2016)may expand outward by a relatively large amount as the 20 intermediateevents are shifted.

Jittering, on the other hand, individually considers a date D_(i) for arespective event “i” (or for a group of events if members of the groupare closely spaced in time), and applies a shift of X_(i) days backwardor forward to each D_(i), within predetermined parameterized boundariesand while avoiding reordering of dates.

Embodiments adjust dates and lower a risk of re-identification byintroducing a controlled and limited amount of date or time jitter, suchthat relative time sequences of the events are preserved, in order toprotect against interval attacks. Time boundaries at the beginning orend of a data set are not expanded significantly. Embodiments make surethat the beginning or end dates expand outward at most by a relativelysmall amount, e.g., by less than +/−3 days as controlled by userconfiguration. This feature may be referred to as constrained bounds.For example, this feature prevents a time range of 365 days beforejittering to grow to, e.g., 400 days, 500 days, or 1,000 days afterjittering. In this way, embodiments help ensure data quality for anygiven interval by preserving consistent cumulative intervals.

Some methods of the background art use random shifting with boundedrandomization of intervals, making the results not necessarilyrepeatable or reversible. Other methods of the background art includeadding random date jitter amounts in an unbounded way, which leads to“date drift”.

In contrast, embodiments protect date intervals by using a deterministicamount of jitter. The deterministic jitter may be calculated by use of ahash function. The deterministic jitter has advantages of repeatabilityand consistency, i.e., for a same set of dates, same project key, andsame patient key, embodiments produce the same output of shifted and/orjittered dates when de-identifying the same data. This allowsembodiments to process incrementally for additional events in time, withminimal additional effort. Thus, embodiments are suitable for streaming,and incrementally updating a patient's profile. “Streaming” as usedherein refers to a near-real time process operating continuously or atdiscrete time intervals (e.g., hourly or daily) that are frequentcompared to how often any one patient's data is expected to change.Streaming at discrete time intervals may also be referred to as“batching”. Newly received streaming data may be added either to thesecond boundary group (defined with respect to FIGS. 3A-3B) if closeenough in time to existing members of the second boundary group, or mayform a new second boundary group (in which case the former secondaryboundary group will become a non-boundary group).

For example, suppose before application of jitter, two groups of eventsare ten days apart. Embodiments would then apply a jitter of plus orminus a few days, the applied jitter being less that the groupseparation of ten days, so that an attacker would be unable tore-identify a person based on the intervals. The amount of jitter toapply may depend on the nature of the analysis to be done (e.g., atime-sensitive analysis may apply relatively less jitter than ananalysis that is not sensitive to time).

Embodiments also include a feature of preserving incremental ordering,i.e., the relative order of events in different groups and within groupswill not change. Preserving incremental ordering ensures that, given afirst event that in reality happened before a second event, does occurafter the second event according to jittered dates.

Embodiments of a jitter process described herein are particularlysuitable for dates, because intervals between dates can yield QIinformation unless the intervals are perturbed. In contrast, differenttechniques are more suitable for other types of patient information. Forexample, a generalization technique may be more useful for patient ageinformation. A clustering process may be more useful for height, weight,or zip code data, and so forth. Generally, to maximize data quality,perturbation processes select an appropriate process by considering whatthe data represents, and then using a process that best supports theanalytic studies, while protecting the patient's identity. Data isperturbed enough that attackers cannot figure out who the datacorresponds to, yet remaining useful for analytic study. Suppression andmasking are also useful tools for de-identification. Each tool is fordifferent purposes.

Embodiments in accordance with the present disclosure provide smartsuppression using improved date jittering. Improved date jittering helpsavoid unnecessary degradation of patient data used for medical studies,and helps avoid release of data that is susceptible tore-identification. Patient privacy is enhanced, and medical studies haveaccess to better quality data.

FIG. 1 illustrates exemplary populations 110, 106, 104 and a sample 102.The sample 102 in this case contains one person, the target. Sample 102represents nine people 104 in the represented population 104, i.e. thetarget looks like eight other people in the represented population 104.

The sample 102 contains a randomly selected person from the prevalentpopulation 106. This is the group of people who could be in the dataset.i.e., if the dataset is about cancer, then the prevalent population 106is all people who have cancer. In this example the prevalence is ⅕, or18 people have breast cancer and could be in the dataset. This group of18 people will be called the prevalent population 106 to indicate therelationship to disease and that population size*prevalence=prevalentpopulation size.

The sample 102 is a subset of the prevalent population 106, one patientin this case, and the one patient looks similar only to half ofprevalent population 106. Thus, k=1, K=9, and N=18, where N is theprevalent population size.

The population 110 contains everyone, even people who do not havecancer. The sampling fraction is defined as the ratio between the sample102 and the prevalent population 106. The represented fraction isdefined as the ratio between the sample 102 and the representedpopulation 104. From this point on, the prevalent population 106 will bereferred to as the population.

In embodiments, Quasi-Identifiers (QIs) are sub-divided into categoriesbased on the largest (i.e., most general) group of people who can know apiece of information, either public information or acquaintanceinformation.

Public—This data is either publically available or the recipient hasthis data. Public data should be structured, accessible to therecipient, and cover a large portion of the population, such as 1% orgreater. A good test for public knowledge is “could the recipient lookup this value for a large percentage of randomly selected people.” Whileself-disclosure and newspapers are public knowledge, they are notstructured and do not cover a large part of the population.

Acquaintance—A person can know this information if they are familiarwith the person or if they see them. Acquaintance level knowledge alsoincludes public information on celebrities and public figures that havetheir personal lives disclosed.

Acquaintance knowledge is not required to be structured or centralized,however it should be knowable by many acquaintances. A good test is“Would at least 50% of your acquaintances know this information?”

FIG. 2 shows a system 200 for performing risk assessment andperturbation of a dataset, in accordance with an embodiment of thepresent disclosure. System 200 executes on a computer including aprocessor 202, memory 204, and input/output interface 206. Memory 204executes instruction for providing a risk assessment module 210, whichperforms an assessment of re-identification risk. The risk assessmentmay also include a de-identification module 216 for performing furtherde-identification of the database or dataset based upon the assessedrisk. A storage device 250, either connected directly to the system 200or accessed through a network (not shown) stores the de-identifieddataset 252 and possibly the source database 254 (from which the datasetis derived) if de-identification is being performed by the system. Adisplay device 230 allows the user to access data and execute the riskassessment process. Input devices such as keyboard and/or mouse provideuser input to the I/O module 206. The user input enables selection ofdesired parameters utilized in performing risk assessment, but may alsobe selected remotely through a web-based interface via network interface234. The instructions for performing the risk assessment may be providedon a computer readable memory. The computer readable memory may beexternal or internal to the system 200 and provided by any type ofmemory such as read-only memory (ROM) or random access memory (RAM). Therisk assessment process can determine a risk for population to sampleand sample to population type attacks in order to aid in determiningquasi-identifier de-identification or anonymization strategies to beapplied to the dataset.

Each element in the embodiments of the present disclosure may beimplemented as hardware, software/program, or any combination thereof.Software codes, either in its entirety or a part thereof, may be storedin a computer readable medium or memory (e.g., as a ROM, for example anon-volatile memory such as flash memory, CD ROM, DVD ROM, Blu-ray™, asemiconductor ROM, USB, or a magnetic recording medium, for example ahard disk). The program may be in the form of source code, object code,a code intermediate source and object code such as partially compiledform, or in any other form.

It would be appreciated by one of ordinary skill in the art that thesystem and components shown in FIG. 2 may include components not shownin the drawings. For simplicity and clarity of the illustration,elements in the figures are not necessarily to scale, are only schematicand are non-limiting of the elements structures. It will be apparent topersons skilled in the art that a number of variations and modificationscan be made without departing from the scope of the invention as definedin the claims.

FIG. 3A illustrates, at a higher of abstraction, a sequence 300 ofintermediate processing states of a data set at three points of a datejittering process in accordance with an embodiment of the presentdisclosure. Each of the three states is represented by a respectivepanel 303, 305, 307. Within each of panels 303, 305, 307, the horizontalX-axis represents time, and the vertical Y-axis represents an ordinalcount.

Panel 303 represents original dates of events for one or more patients.The data is illustrated as being segmented into five groups. The fivegroups represent patient data selected because the patients and/or theevents satisfy predetermined traits or other selection criteria. Theevents for each of the five groups is represented by groups 301 a . . .301 e, respectively.

Panel 305 represents the result of a simple time shift, i.e., timeshifting all events of panel 303 by a fixed amount of time. Thisrepresents a simple shift. All events illustrated in panel 305 maintainthe same time spacing between events, compared to the time spacingbetween corresponding events in panel 303. The time shift of panel 305protects against an attacker who knows exact dates pertinent to apredetermined patient.

Panel 307 represents exemplary results of applying a time jitter toevents on a group-by-group basis in accordance with an embodiment of thepresent disclosure. The applied time jitter may be positive, negative,or zero. The time shift of panel 307 protects against an attacker whoknows exact intervals between dates pertinent to a predeterminedpatient. For example, group 301 a and group 301 e have had zero timejitter applied. Groups 301 b and 301 c have had a negative time jitterapplied, and group 301 d has had a positive time jitter applied. Jitteris applied such that the relative order of events does not change ingoing from panel 303 to panel 307. Furthermore, since jitter may beapplied equally to all members of a group, the event-to-event timespacing of members within a group may be invariant.

In some embodiments, the time jitter illustrated in panel 307 may beperformed before the time shift of panel 305.

FIG. 3B illustrates a process 350 in accordance with an embodiment ofthe present disclosure. Process 350 corresponds to sequence 300 ofintermediate processing states. Process 350 begins at step 351, at whichthe data is partitioned into time-separated groups. Next, process 350progresses to step 353, at which each group is time shifted by a fixedamount. Next, process 350 progresses to step 355, at which for eachnon-boundary group a positive and negative (i.e., forward and backward,respectively) jitter limit is determined. Next, process 350 progressesto step 357, at which for each non-boundary group a hash function isused to calculate a candidate jitter amount. Next, process 350progresses to step 359, at which for each non-boundary group an amountof jitter is applied that is the lesser of the candidate jitter amountor the jitter limit. When calculating “lesser”, a positive candidatejitter is compared to the positive jitter limit. For a negativecandidate jitter, the absolute value of the candidate jitter is comparedto the absolute value of the negative jitter limit.

FIG. 4A illustrates, at a lower level of abstraction, a sequence 400 ofintermediate processing states of a data set at six points of a datejittering process in accordance with an embodiment of the presentdisclosure. Each of the six states is represented by a respective panel401-409. Within each of panels 401-409, the horizontal X-axis representstime, and the vertical Y-axis represents an ordinal count.

In overview of sequence 400, jitter bounds are predefined, and may bebased at least in part upon user input. For example, jitter bounds fordates may undergo a simple shift of up to one month; so jitter boundsmay be up to 15 days. The entire sequence may have been shifted by 15days forwards, or backwards, providing an ambiguity of each data ofabout a month.

Embodiments recognize that if an exact interval between two dates isprecise and invariant after perturbing the date, then a sequence hasbeen shifted but the intervals have not been shifted. Embodiments applyjitter to the dates, such that the dates may be jittered by a smallamount of time, e.g., +/−three days. The small jitter limit may beuser-configurable to different limits (e.g., +/−7 days) depending onfactors such as the number of dates, density of dates, importance of thedata, how the data will be used, and so forth. Embodiments also mayconsider other conditions, e.g., dates that are within three days ofeach other may be grouped together, or events within two days of eachother may be grouped together.

Sequence 400 begins with panel 401, at which events that are close intime (e.g., within about one day of another group member) are groupedtogether. It is sufficient that each event is within one day of at leastone other event. For example, ten separate events spanning ten days (oneevent per day) may be considered a single group. Groups may have aminimum of one member. Groups are illustrated in FIG. 4A by dottedboxes. Exemplary groups 421 a-421 e are marked in panel 401, but forsake of clarity not all groups in FIG. 4A are marked with a referencenumber. Preferably, groups should be separated by a time gap of morethan one day. In some embodiments, a time gap between groups will belarger than at least some of the time separations between events withinthe two groups on either side of the time gap. Events within groups maybe shifted together to increase their associated analytic value.Shifting events this way increases their analytic value because it isassumed that for closely related dates, the analytic value associatedwith the interval is high. For instance, if a patient is admitted to ahospital one day and is discharged from the hospital the next day, themeaning of the hospital stay is significantly different from a patientwho is admitted to a hospital one day and is discharged from thehospital three days later. For this reason, embodiments group togetherclosely related dates in order to maintain their intervals.

Groups may be either boundary groups or non-boundary groups. Boundarygroups are groups at the ends of an entire set of events being analyzed.With respect to the entire set of events illustrated in panel 401, thefirst group in time (i.e., group 421 a) is one boundary group, the lastgroup in time (i.e., group 421 e) is a second boundary group.Non-boundary groups are all other groups that are not a boundary group.

In order to assure and to preserve incremental ordering of events,embodiments do not jitter boundary groups since a time gap before afirst boundary group or a time gap after a last boundary group cannot beknown. Thus, for each non-boundary group, embodiments perform theprocessing described below with respect to panels 403-407B, as indicatedby dotted box 411.

At panel 403, time gaps are determined and recorded between eachboundary group and the groups adjacent to it. Each non-boundary group isconsidered in order, e.g., an ascending time order. For example, fornon-boundary group 421 b, a first time gap 431 is determined between thepresent group 421 b and the previous group 421 a, and a second time gap433 between the present group 421 b and the next group 421 c. For sakeof clarity, not all time gaps are marked in panel 403 with a referencenumber.

Time gap 431 may be referred to as a maximum backward jitter shift, andtime gap 433 may be referred to as a maximum forward date jitter forgroup 421 b. Time gaps 431, 433 represent an amount of time that group421 b may be jittered, as indicated by the range illustrated betweenpanel 405 and panel 407 a. Embodiments may further limit a range ofjittering by comparing the first and second time gaps to respectivepredetermined forward and backward jitter shift parameters. The forwardand backward jitter shift parameters represent a maximum amount ofjitter, and limiting jitter increases the analytic value of jittereddates, at the expense of a higher risk of re-identification. The valueof the forward and backward jitter shift parameters is predeterminedbased upon an analytic tradeoff, for a specific project, betweenanalytic value and risk of re-identification. Therefore, the value ofthe forward and backward jitter shift parameters is a function of adesired analytic value and risk of re-identification, and conversely theanalytic value and risk of re-identification is a function of the valueof the forward and backward jitter shift parameters.

Embodiments then may take the minimum of each value (i.e., bycalculating MIN(first time gap, backward jitter shift parameter) andMIN(second time gap, forward jitter shift parameter)), and cap anyjitter applied to the group by the minimum of each value. This isequivalent to calculating a jitter amount for the group by firstcalculating an initial jitter amount, then capping the initial jitter byboth the numeric value of the first and second time gaps and by therespective predetermined forward and backward jitter shift parameters.

The amount of jitter may be calculated by use of a deterministicfunction such as a hash function. For example, embodiments in accordancewith the present disclosure may take a secret key (e.g., a cryptographickey) and a respective date value within each group (e.g., the firstdate) as inputs to a combination hash and encryption function. Incontrast, some techniques of the background art use a random numbergenerator to determine an amount of jitter. Usage of a deterministicfunction such as a hash facilitates embodiments having predictable andreproducible amounts of jitter and shift. Hashing allows embodiments tosupport predictable data updates. This allows simple shifting to beconsistent even in a streaming scenario.

The secret key may include one or more sub-keys, such as a projectsub-key that is specific to the project, and/or an entity sub-key thatis specific to the entity (e.g., a patient) whose data is beingprocessed. The project sub-key is a cryptographic key assigned to aproject at the inception of the project, and remains associated with theproject throughout the duration of the project. For example, the projectsub-key may be a 128-bit randomly-generated universally uniqueidentifier (UUID) by default, but a user may override the defaultproject sub-key with substantially any arbitrary string. The entitysub-key depends on the data and may be a medical record number, anumeric identifier, or substantially any arbitrary string.

The hash function takes in the secret key (i.e., project and patientsub-keys) as well as the first date in the group to be jittered, andreturns a long integer value, e.g., in range [−2⁶³, 2⁶³ −1]. To ensurethat embodiments return a value within an acceptable range, embodimentsmap the returned hash value to a value within the prescribed range(i.e., [−jitter, +jitter]) using modulo arithmetic. For example, ifembodiments use a jitter parameter of three days, but there was room toshift forward by only two days without colliding with another group,then the jitter range would be [−3, 2], which has a size of six days. Toensure that a hash value “H” falls within this jitter range, embodimentscalculate the value of H modulo the jitter range (i.e., H mod 6) to givea value in the modulo range [0, 5]. The modulo range is then shifted tocoincide with the jitter range (e.g., in this example by adding thestarting value “−3” of the jitter range) to produce a value within therange [−3, 2].

In some embodiments, unequal amounts of jitter may be applied atdifferent times for the same patient, depending upon the nature of thedata. This may be illustrated as, e.g., a first set of limits for afirst non-boundary group and a second set of limits for a secondnon-boundary group, such that the first set of limits is different thanthe second set of limits. For example, during a study of pregnancy,events during the first trimester may be jittered less than eventsduring the third trimester, if the study is about events that are mostimpactful early in a pregnancy.

Next, panel 407 b illustrates a computed amount of date jitter beingapplied to events within group 421 b. Once a respective calculated datejitter is calculated and applied to each non-boundary group of FIG. 4A,the resulting date-jittered data set is outputted, as illustrated inpanel 409.

FIG. 4B illustrates a process 450 in accordance with an embodiment ofthe present disclosure. Process 450 corresponds to sequence 400 ofintermediate processing states. Process 450 begins at step 451, at whichthe data is partitioned into time-separated groups. Next, process 450progresses to step 453, at which time intervals are measured betweeneach non-boundary group and the groups next to it. Next, process 450progresses to step 455, at which for each non-boundary group a positiveand negative (i.e., forward and backward, respectively) jitter limit isdetermined. Next, process 450 progresses to step 457, at which for eachnon-boundary group a hash function is used to calculate a candidatejitter amount. Next, process 450 progresses to step 459, at which foreach non-boundary group an amount of jitter is applied that is thelesser of the candidate jitter amount or the jitter limit. Whencalculating “lesser”, a positive candidate jitter is compared to thepositive jitter limit. For a negative candidate jitter, the absolutevalue of the candidate jitter is compared to the absolute value of thenegative jitter limit.

Embodiments in accordance with the present disclosure are applicable tosensitive or confidential information other than medical data. Forexample, embodiments may be applied to salary data, surveillancerecords, and so forth.

Embodiments of the present disclosure include a system having one ormore processing units coupled to one or more memories. The one or morememories may be configured to store software that, when executed by theone or more processing unit, allows practice of the embodimentsdescribed herein, at least by use of processes described herein,including at least in FIGS. 3A-4B, and related text.

The disclosed methods may be readily implemented in software, such as byusing object or object-oriented software development environments thatprovide portable source code that can be used on a variety of computeror workstation platforms. Alternatively, the disclosed system may beimplemented partially or fully in hardware, such as by using standardlogic circuits or VLSI design. Whether software or hardware may be usedto implement the systems in accordance with various embodiments of thepresent disclosure may be dependent on various considerations, such asthe speed or efficiency requirements of the system, the particularfunction, and the particular software or hardware systems beingutilized.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the present disclosure maybe devised without departing from the basic scope thereof. It isunderstood that various embodiments described herein may be utilized incombination with any other embodiment described, without departing fromthe scope contained herein. Further, the foregoing description is notintended to be exhaustive or to limit the disclosure to the precise formdisclosed. Modifications and variations are possible in light of theabove teachings or may be acquired from practice of the disclosure.Certain exemplary embodiments may be identified by use of an open-endedlist that includes wording to indicate that the list items arerepresentative of the embodiments and that the list is not intended torepresent a closed list exclusive of further embodiments. Such wordingmay include “e.g.,” “etc.,” “such as,” “for example,” “and so forth,”“and the like,” etc., and other wording as will be apparent from thesurrounding context.

No element, act, or instruction used in the description of the presentapplication should be construed as critical or essential to thedisclosure unless explicitly described as such. Also, as used herein,the article “a” is intended to include one or more items. Where only oneitem is intended, the term “one” or similar language is used. Further,the terms “any of” followed by a listing of a plurality of items and/ora plurality of categories of items, as used herein, are intended toinclude “any of,” “any combination of,” “any multiple of,” and/or “anycombination of multiples of” the items and/or the categories of items,individually or in conjunction with other items and/or other categoriesof items.

Moreover, the claims should not be read as limited to the describedorder or elements unless stated to that effect. In addition, use of theterm “means” in any claim is intended to invoke 35 U.S.C. § 112(f), andany claim without the word “means” is not so intended.

What is claimed is:
 1. A computer-implemented method, the methodcomprising: receiving a data query to request information on data formultiple patients; assigning the data for the multiple patients into aplurality of patient groups in response to the data query, wherein eachpatient is assigned to a single group of the plurality of patientgroups; determining a forward deviation limit and a backward deviationlimit for each of the patient groups based on a time deviation ofpatient data in the plurality of patient groups; determining a deviationamount for one or more of the patients based on the determined forwarddeviation limit and the backward deviation limit, wherein a hashfunction provides security to information associated with the one ormore patients; and providing the data for the multiple patientsincluding the determined deviation amount.
 2. The method of claim 1,further comprising: providing an additional time deviation to thepatient data after calculating the deviation amount for the one or morepatients.
 3. The method of claim 1, further comprising: shifting eventsassociated with the patient data by a fixed amount of time beforeapplying the time deviation to the patient data in the plurality ofpatient groups to protect the information associated with the one ormore patients.
 4. The method of claim 1, further comprising: comparingthe deviation amount of the one or more patients to the forwarddeviation limit after calculating the deviation amount of the one ormore patients to determine a level of the security provided to theinformation associated with the one or more patients.
 5. The method ofclaim 1, wherein the patient data into is partitioned intotime-separated groups when each of the patients are assigned to theirsingle patient group.
 6. The method of claim 1, wherein the timedeviation is applied to prevent an attacker from obtaining knowledge onexact intervals between dates relevant to the one or more patients. 7.The method of claim 1, further comprising: comparing the deviationamount for the one or more patients to the backward deviation amount todetermine a level of the security provided to the information associatedwith the one or more patients.
 8. A computer program product comprisinga tangible storage medium encoded with processor-readable instructionsthat, when executed by one or more processors, enable the computerprogram product to: receive a data query to request information on datafor multiple patients; assign the data for the multiple patients into aplurality of patient groups in response to the data query, wherein eachpatient is assigned to a single group of the plurality of patientgroups; determine a forward deviation limit and a backward deviationlimit for each of the patient groups based on the time deviation appliedto the patient data in the plurality of patient groups; calculate adeviation amount for one or more of the patients based on the determinedforward deviation limit and the backward deviation limit, wherein a hashfunction is utilized to calculate the deviation amount for the one ormore patients, and provide security to information associated with theone or more patients; and provide the data for the multiple patientsincluding the calculated deviation amount.
 9. The computer programproduct of claim 8, wherein the calculated deviation amount for the oneor more patients is a positive deviation amount.
 10. The computerprogram product of claim 8, wherein the calculated deviation amount forthe one or more patients is a negative deviation amount.
 11. Thecomputer program product of claim 8, wherein the calculated deviationamount for the one or more patients is a positive deviation amount thatis compared to the forward deviation limit to identify a level of thesecurity provided to the information associated with the one or morepatients.
 12. The computer program product of claim 8, wherein zero timedeviation is applied to at least one of the patient groups when the timedeviation is applied to the patient data in the plurality of patientgroups.
 13. The computer program product of method of claim 8, wherein atime shift is applied to events associated with the one or more patientsto prevent access to the information associated with the one or morepatients before the time deviation is applied.
 14. The computer programproduct of claim 8, wherein the calculated deviation amount for the oneor more patients is a negative deviation amount that is compared to thebackward deviation limit.
 15. A computer system connected to a network,the system comprising: one or more processors configured to: receive adata to request information on data for multiple patients; assign thedata for the multiple patients into a plurality of patient groups inresponse to the data query, wherein each patient is assigned to a singlegroup of the plurality of patient groups; determine a forward deviationlimit and a backward deviation limit for each of the patient groupsbased on the time deviation applied to the patient data in the pluralityof patient groups; calculate a deviation amount for one or more of thepatients based on the determined forward deviation limit and thebackward deviation limit, wherein a hash function is utilized tocalculate the deviation amount for the one or more patients, and providesecurity to information associated with the one or more patients; andprovide the data for the multiple patients including the calculateddeviation amount.
 16. The system of claim 15, wherein the patient datain the plurality of patient groups receive a positive time deviation ora negative time deviation when the time deviation is applied to thepatient data in the plurality of groups.
 17. The system of claim 15,wherein a time shift is applied to events associated with the one ormore patients before the time deviation is applied to the patient datain the plurality of patient groups to prevent access to the informationpertinent to the one or more patients.
 18. The system of claim 15,wherein the time deviation is applied to prevent one or more partiesfrom identifying intervals between dates relevant to the one or morepatients.
 19. The system of claim 15, wherein the calculated deviationamount for the one or more patients is compared to the forward deviationlimit.
 20. The system of claim 15, wherein the calculated deviationamount for the one or more patients is compared to the backwarddeviation limit.